Data engineering is the process of designing and building systems that collect, store and process data for use in analytics and decision-making. It is the backbone of any data-driven organization, as it ensures that data flows smoothly, is properly structured and is ready for analysis.

A data engineer works across the entire data lifecycle, from raw data collection to the creation of data pipelines, ensuring that information is accurate, accessible and organized. This allows data scientists and analysts to focus on extracting insights, building models and deriving business value.

Data engineers are in charge of building the infrastructure for data to flow correctly, as well as enable aspects such as data consolidation, the combining and storing of varied data in a single place. The analysts and scientists then consume what the engineer prepared.

Data scientists process and develop data models and then deploy them into applications using, among other things, machine learning algorithms. Data scientists have advanced knowledge of languages such as Python, R and SAS, as well as have a good understanding of statistics.s

At GAP, we’ve successfully completed over ### data engineering projects across industries like FinTech, HealthTech, eCommerce and more. We approach every data engineering project with a focus on collaboration, customization and efficiency understanding that each organization’s data needs are unique. Our focus is to work closely with your team to design and implement a tailored data engineering solution that meets your business goals. Here are some examples of what we can help you solve:

• Data collection, data cleansing and data warehousing
• Structured and efficient data retrieval
• Governing the use and accessibility of data
• Integrating AI into trading algorithms for faster and more accurate decision-making
• Ensuring data accuracy, consistency and availability
• Implementing strategies and technologies to achieve data governance and regulatory compliance
• Ensuring data protection, privacy and disaster recovery
• Automating customer support with chatbots